In a semiconductor manufacturing process, in order to increase a yield rate, it is important to quickly determine a cause of occurrence of a defect on a semiconductor wafer. In existing circumstance, in a semiconductor manufacturing site, a defect is analyzed by using a defect checking apparatus and a defect observing apparatus.
The defect checking apparatus is an apparatus that observes a wafer by using an optical means or an electron beam and that outputs position coordinates of a detected defect. Since it is important for the defect checking apparatus to perform high speed processing on a broad range, reduction in image data amount by increasing pixel sizes (that is, conversion to low resolution) of an image to be acquired as much as possible is performed. In many cases, if the existence of the defect can be recognized from a detected low resolution image, it is difficult to identify the type (defect type) of the defect in detail.
Accordingly, a defect observing apparatus is used. The defect observing apparatus is an apparatus that uses output information of the defect checking apparatus to perform imaging on coordinates of a defect on a wafer at a high resolution, and that outputs an image. In the semiconductor manufacturing process, micronizing is proceeded with, and also defect size accordingly reaches an order of several tens of nm. In order to observe the defect in detail, a resolution in an order of several nm is necessary. Accordingly, in recent years, a defect observing apparatus (review SEM) using a scanning electron microscope (SEM) is widely used. The review SEM has an ADR (Automatic Defect Review) function of automatically collecting high resolution images (defect images) of defects on a wafer by using defect coordinates output by the defect checking apparatus.
The captured SEM images are classified on the basis of the types of imaged defects, and the like. In a classification operation, a user recognizes the images, and adds, to each image, a class (user class) to be added by the user. The above class adding operation is often manually done by the user, and is called manual classification. Manual classification of defect images is called manual defect classification (MDC). Note that in classification of SEM images, for the purpose of specifying a circuit pattern in which a defect easily occurs, the images may be classified on the basis of circuit patterns in on-image defect portions and around the defect portions.
Since in recent years, a throughput of the ADR of the review SEM increases, also a function that automates an operation of identifying defect types from a large quantity of collected defect images has been proposed. The review SEM is provided with a classifying ADC (automatic defect classification) function for automatically identifying and classifying defect types from defect images.
As one method of automatic classification, a method that quantifies appearance feature amounts of defect regions by image processing to perform classification using a neural network is disclosed in PTL 1 (JP 8-21803 A). In the automatic classification, classification is performed in accordance with classification recipes. The classification recipes includes various types of parameters such as image processing parameters, information (classification classes) on defect types to be classified, and defect images (instruction images) belonging to the respective classification classes. The instruction images are necessary for giving classification criteria of the respective classes to the automatic classification, and are basically given by the MDC. Classification results by the MDC greatly influence the performance of the ADC. Thus, the manual classification remains placed at an important position.
In addition, in the image classification, as a classification technique using image feature amounts obtained by image processing, a technique that performs unsupervised classification on images and simultaneously add classes to classification results is disclosed in PTL 2 (JP 2008-516259 W) and PTL 3 (U.S. Pat. No. 6,999,614).